Computation via Neuron-like Spiking in Percolating Networks of Nanoparticles.

Studholme, SJ; Heywood, ZE; Mallinson, JB; Steel, JK; Bones, PJ; Arnold, MD; Brown, SA

Computation via Neuron-like Spiking in Percolating Networks of Nanoparticles.

Studholme, SJ Heywood, ZE Mallinson, JB Steel, JK Bones, PJ Arnold, MD Brown, SA

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Publisher:: AMER CHEMICAL SOC
Publication Type:: Journal Article
Citation:: Nano Lett, 2023, 23, (22), pp. 10594-10599
Issue Date:: 2023-11-22

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Field	Value	Language
dc.contributor.author	Studholme, SJ
dc.contributor.author	Heywood, ZE
dc.contributor.author	Mallinson, JB
dc.contributor.author	Steel, JK
dc.contributor.author	Bones, PJ
dc.contributor.author	Arnold, MD
dc.contributor.author	Brown, SA
dc.date.accessioned	2024-01-16T01:26:20Z
dc.date.available	2024-01-16T01:26:20Z
dc.date.issued	2023-11-22
dc.identifier.citation	Nano Lett, 2023, 23, (22), pp. 10594-10599
dc.identifier.issn	1530-6984
dc.identifier.issn	1530-6992
dc.identifier.uri	http://hdl.handle.net/10453/174542
dc.description.abstract	The biological brain is a highly efficient computational system in which information processing is performed via electrical spikes. Neuromorphic computing systems that work on similar principles could support the development of the next generation of artificial intelligence and, in particular, enable low-power edge computing. Percolating networks of nanoparticles (PNNs) have previously been shown to exhibit critical spiking behavior, with promise for highly efficient natural computation. Here we employ a rate coding scheme to show that PNNs can perform Boolean operations and image classification. Near perfect accuracy is achieved in both tasks by manipulating the spiking activity using certain control voltages. We demonstrate that the key to successful computation is that nanoscale tunnel gaps within the percolating networks transform input data through a powerful modulus-like nonlinearity. These results provide a basis for implementation of further computational schemes that exploit the brain-like criticality of these networks.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	AMER CHEMICAL SOC
dc.relation.ispartof	Nano Lett
dc.relation.isbasedon	10.1021/acs.nanolett.3c03551
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.rights	This document is the Accepted Manuscript version of a Published Work that appeared in final form in Nano Lett, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see Computation via Neuron-like Spiking in Percolating Networks of Nanoparticles on Request author-directed link to Published Work, See https://pubs.acs.org/doi/10.1021/acs.nanolett.3c03551
dc.subject.classification	Nanoscience & Nanotechnology
dc.title	Computation via Neuron-like Spiking in Percolating Networks of Nanoparticles.
dc.type	Journal Article
utslib.citation.volume	23
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2024-11-13T00:00:00+1000Z
dc.date.updated	2024-01-16T01:26:19Z
pubs.issue	22
pubs.publication-status	Published
pubs.volume	23
utslib.citation.issue	22

Abstract:

The biological brain is a highly efficient computational system in which information processing is performed via electrical spikes. Neuromorphic computing systems that work on similar principles could support the development of the next generation of artificial intelligence and, in particular, enable low-power edge computing. Percolating networks of nanoparticles (PNNs) have previously been shown to exhibit critical spiking behavior, with promise for highly efficient natural computation. Here we employ a rate coding scheme to show that PNNs can perform Boolean operations and image classification. Near perfect accuracy is achieved in both tasks by manipulating the spiking activity using certain control voltages. We demonstrate that the key to successful computation is that nanoscale tunnel gaps within the percolating networks transform input data through a powerful modulus-like nonlinearity. These results provide a basis for implementation of further computational schemes that exploit the brain-like criticality of these networks.

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http://hdl.handle.net/10453/174542